Activity: IT Audit Test Preparation

Activity: IT Audit Test Preparation

Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-7998-4198-2.ch005
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Audit testing objectives, frequently, are determined in the preceding audit processes: planning as well as study and evaluation of controls. IT audit test materiality influences the audit testing nature, timing, and extent. In designing tests, the in-charge auditor must choose between statistical and non-statistical testing methodologies. Compliance and substantive testing may take the form of inquiry, observation, inspection, or re-performance. Sampling method selection reflects whether audit area statistical inferences are going to occur concerning the target population. Sampling risk, acceptable error rate, and the expected extent of errors in the population are sample size consideration factors. Chapter 5 conveys how to determine IT audit test objectives, test materiality, test methods, test designs, and designing audit tests.
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Testing Alignment

An IT audit test is a basis for assessing audit area conditions. IT audit testing requires the assigned IT auditor to execute prepared procedures and inscribe test outcomes for critical examination (Davis, 2011). IT audit test plans address preventive, detective, and corrective controls (Davis, 2005, 2011). Presumably, audit testing selection exemplifies the in-charge IT auditor’s desire to assess CR at less than the maximum level (Davis, 2005, 2011). Therefore, testing IT controls is an operating effectiveness issue when performing IT audit engagements (Davis, 2005, 2011).

Key Terms in this Chapter

Random Seed: Is the starting value used to produce a random number sequence.

Type I Error: In the field of statistics, the incorrect rejection of the null hypothesis (also known as an Alpha Error).

Population: Encompasses a group of items for examination consideration during audit testing.

Sample Frame: Depicts a population physical sample representation.

Type II Error: In the field of statistics, the incorrect acceptance of the null hypothesis (also known as a Beta Error).

Sampling Unit: Represents one individual item within the IT auditable unit test population.

Random Number: Has no predictable relationship to any other number or event.

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